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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRSDate not supplied

Use of Design of Experiments and Rule-Based Inference in Determining Neural Network Architectures for Loss of Control Detection

In this work, we describe methods for selecting the neural network architectures and input spaces to implement belief state inference on generic commercial transport aircraft. First, we highlight a case study on the planning, execution, and analysis of a set of experiments to determine the configurations of a conditional variational autoencoder (CVAE). We present a structured method that can be used in a number of aerospace applications, to optimize the structure and training parameters of the CVAE for belief state inference, using Design of Experiments (DOE) statistical methodologies. The motivation for this specific DOE was to identify the appropriate hyperparameters for measuring the CVAE reconstruction probability and latent space, such that the measurements can be used to infer qualitative state changes for the aircraft. We demonstrate that this process yields information about a trained neural network’s utility for this specific application, along with a quantifiable range of certainty. We execute 84 experiments using loss-of-control flight maneuver data from a NASA T-2 aircraft, demonstrating that this empirical process allows us to construct cheap and simple models with specific attributes amenable to belief state inference in aerospace applications. While theoretically, we could create a single CVAE with an input space the size of all measurable flight variables and environmental dynamics, it becomes intractable to use such a neural network in an in-situ intelligent multi-agent system. Using the recommendations from our case study, we introduce a technical approach for feasibly describing the belief space by (1) identifying significant statistical relationships among flight variables using rule induction, (2) using a set of rules that cover all features to define the input space of multiple CVAEs, and (3) forming a belief space based on the joint probability density of their collective latent spaces. This results in a series of relatively small matrix multiplications that can be performed in real time, as opposed to large matrix computations in a single CVAE. We demonstrate the application of this approach on the T-2 flight loss-of control experiments, using the architecture and hyperparameter recommendations from the case study. We compare the utilities of an individual CVAE trained on all flight variables and multiple CVAEs defined on subsets of flight variables for detecting qualitative changes in flight. We demonstrate that the use of multiple CVAEs with smaller input spaces permits the CVAE to capture more granular relationships in the latent space, permitting better state space characterization and loss-of-control detection.

Design of experiments↗

NASA NTRS1989

Intelligent control of robotic arm/hand systems for the NASA EVA retriever using neural networks

Adaptive/general learning algorithms using varying neural network models are considered for the intelligent control of robotic arm plus dextrous hand/manipulator systems. Results are summarized and discussed for the use of the Barto/Sutton/Anderson neuronlike, unsupervised learning controller as applied to the stabilization of an inverted pendulum on a cart system. Recommendations are made for the application of the controller and a kinematic analysis for trajectory planning to simple object retrieval (chase/approach and capture/grasp) scenarios in two dimensions.

Mclauchlan, Robert A.↗

NASA NTRS2024

Recommendations on Evidence and Process for Certification of Learning-enabled Components in Aerospace Systems

This report primarily identifies a collection of relevant and necessary evidence for assurance of machine learnt components (MLCs)—also known as learning-enabled components—integrated into aircraft systems, and gives preliminary suggestions on the elements of a certification process that invoke the identified evidence. The main focus is on feedforward neural networks that are static and trained offline through supervised learning. A brief background on the generic elements of the lifecycle of an MLC is given to contextualize the assurance considerations and, consequently, the evidence that is relevant and necessary to support certification. At the level of an MLC, those considerations relate to: (i) the consistency and correctness of MLC contributions to system functions in the context of a validated functional intent; and (ii) the absence of MLC contributions to aircraft-level failure conditions. At an ML model level, confidence in model and data properties contribute to assurance of the containing MLC, in particular: (a) generalizability and robustness of models, in the presence of inputs not previously seen during training, disturbances to inputs, and unexpected inputs; and (b) valid data, i.e., data that are at least representative, relevant, complete, and accurate. Evidence for the above span the elements of the ML lifecycle, and includes, at a minimum, lifecycle artifacts that pertain to: (1) properties of requirements capturing functional intent, safety constraints, and aspects of the intended use and operating environment; (2) model performance, model complexity and design, and algorithm choice; (3) achievement of required performance at the levels of a trained model during model development, a trained model after model development is complete, and a trained model that is transformed into an executable equivalent; (4) model implementation aspects necessary for transforming a trained model into the executable equivalent; (5) integration of the executable trained model into the containing MLC, and eventually the larger system; and, (6) lastly, the verification and validation (V&V) of each of the above. Such V&V lifecycle artifacts themselves include: aspects of coverage, e.g., of various levels of requirements by the input space of the model and the data; traceability (where applicable); application of formal methods for property specification, analysis, and checking. Examples of evidence generation methods and tools further ground the discussion on what constitutes evidence, and the contribution to assurance during certification. The identified assurance considerations and supporting evidence is not a comprehensive set. Additionally, neither what should be considered as sufficient evidence relative to the assigned criticality of an MLC, nor how criticality ought to be determined and adjusted, have been considered in this report. However, suggestions are made for potential activities of the ML lifecycle that are aimed at providing confidence that an MLC can be relied upon when integrated into its containing (aircraft) system. Those activities are proposed as candidate elements of a certification process for MLCs. The main purpose of this report to inform regulatory guidance and consensus standards that may be used to meet the safety intent of the applicable regulations.

Aviation safety↗

NASA NTRS2017

Advances in Medical Analytics Solutions for Autonomous Medical Operations on Long-Duration Missions

A review will be presented on the progress made under STMDGame Changing Development Program Funding towards the development of a Medical Decision Support System for augmenting crew capabilities during long-duration missions, such as Mars Transit. To create an MDSS, initial work requires acquiring images and developing models that analyze and assess the features in such medical biosensor images that support medical assessment of pathologies. For FY17, the project has focused on ultrasound images towards cardiac pathologies: namely, evaluation and assessment of pericardial effusion identification and discrimination from related pneumothorax and even bladder-induced infections that cause inflammation around the heart. This identification is substantially changed due to uncertainty due to conditions of fluid behavior under space-microgravity. This talk will present and discuss the work-to-date in this Project, recognizing conditions under which various machine learning technologies, deep-learning via convolutional neural nets, and statistical learning methods for feature identification and classification can be employed and conditioned to graphical format in preparation for attachment to an inference engine that eventually creates decision support recommendations to remote crew in a triage setting.

Medical Decision Support Systems↗

NASA NTRS1992

Evaluation of tests for vestibular function

Vestibular testing methodologies are examined in terms of their clinical value and applicability to the assessment of astronaut candidates and human responses to microgravity. Classical and novel testing approaches are examined in terms of nine functions such as otolith organs, vestibular neural processing, and ocular motor systems. The tests reviewed include the basic vestibular-function test battery, postural stability testing, visual-vestibular interaction, reactions to off-axis rotation, and vestibular evoked responses. The basic test battery is shown to be essential for astronaut and inertial-environment baseline-data evaluations, whereas the newer and research tests are only recommended in special cases. Stimulus rearrangement and motion-sickness tests are indicated for testing adaptation to inertial environments. Postural stability and visual-vestibular interaction are recommended for evaluating one's suitability for being an astronaut.

Source record↗

NASA NTRS2005

Training Software in Artificial-Intelligence Computing Techniques

The Artificial Intelligence (AI) Toolkit is a computer program for training scientists, engineers, and university students in three soft-computing techniques (fuzzy logic, neural networks, and genetic algorithms) used in artificial-intelligence applications. The program promotes an easily understandable tutorial interface, including an interactive graphical component through which the user can gain hands-on experience in soft-computing techniques applied to realistic example problems. The tutorial provides step-by-step instructions on the workings of soft-computing technology, whereas the hands-on examples allow interaction and reinforcement of the techniques explained throughout the tutorial. In the fuzzy-logic example, a user can interact with a robot and an obstacle course to verify how fuzzy logic is used to command a rover traverse from an arbitrary start to the goal location. For the genetic algorithm example, the problem is to determine the minimum-length path for visiting a user-chosen set of planets in the solar system. For the neural-network example, the problem is to decide, on the basis of input data on physical characteristics, whether a person is a man, woman, or child. The AI Toolkit is compatible with the Windows 95,98, ME, NT 4.0, 2000, and XP operating systems. A computer having a processor speed of at least 300 MHz, and random-access memory of at least 56MB is recommended for optimal performance. The program can be run on a slower computer having less memory, but some functions may not be executed properly.

Howard, Ayanna↗

NASA NTRS1992

Application of fuzzy logic-neural network based reinforcement learning to proximity and docking operations: Attitude control results

As part of the RICIS activity, the reinforcement learning techniques developed at Ames Research Center are being applied to proximity and docking operations using the Shuttle and Solar Max satellite simulation. This activity is carried out in the software technology laboratory utilizing the Orbital Operations Simulator (OOS). This report is deliverable D2 Altitude Control Results and provides the status of the project after four months of activities and outlines the future plans. In section 2 we describe the Fuzzy-Learner system for the attitude control functions. In section 3, we provide the description of test cases and results in a chronological order. In section 4, we have summarized our results and conclusions. Our future plans and recommendations are provided in section 5.

Jani, Yashvant↗

NASA NTRS2020

Electromagnetic Pain Relief/Blocking: Feasibility Assessment

Context/Background: Astronauts use pharmaceuticals during spaceflight to manage acute and chronic pain, but use of analgesics will have drawbacks for exploration-class missions because the shelf life of these medications is limited, resupply will be curtailed, astronauts may develop tolerance and/or addiction to these medications, and side effects can include impairment of cognitive abilities. Electromagnetic devices have been developed that treat pain terrestrially by affecting neuromodulation–dubbed “electroceuticals”, these devices have varied mechanisms of action that either stimulate or suppress neural activity in the central nervous system or peripheral nerves. Objective/Purpose: The available literature was reviewed and FDA-approved pain treatments (both pharmacological and non-pharmacological), as well as those currently under development, were assessed for their suitability for use in exploration class spaceflight missions. Data Sources: Due to the COVID-19 pandemic and the resulting closure of libraries, data sources were restricted to those available digitally. Online database searches included PubMed, U.S. Patent and Trademark Office, federal grant award databases (National Aeronautics and Space Administration (NASA), Department of Defense (DoD), National Institutes of Health (NIH)), and general internet searches. More than 1,600 records were reviewed in this effort. Study Selection/Eligibility Criteria: Targeted searches included different aspects of pain management. Priority was given to review studies, to cover as much of the available literature as possible in this limited effort. Study Appraisal and Synthesis Methods/Data Extraction and Data Synthesis: The titles of the studies and the awards that were obtained by searching online databases were reviewed and further information was sought for the relevant titles. Abstracts or award summaries were generally available online; for journal abstracts, full text articles were either available online or were requested via interlibrary loan. Results: An overwhelming majority of the literature focuses on the treatment of chronic rather than acute pain because it is assumed that acute pain only rarely fails to resolve and instead transitions into chronic pain when the central nervous system becomes hypersensitized. The available electromagnetic devices marketed for pain treatment have varying levels of invasiveness, use different mechanisms of action, and have demonstrated varying efficacy when evaluated scientifically. A truly noninvasive, highly efficient device is desired for use during spaceflight. One portable, self-contained, FDA-approved device was identified that, from preliminarily assessment, best met these criteria; the device noninvasively applies pulsed shortwave therapy (PSWT) to modify pain signals from peripheral nerves, however, the device has limited battery life and the effects are relatively non-selective in type of neural signal modified. Limitations: This current effort, although extensive, did not identify a comprehensive list of all alternatives for pain treatment. Once the pandemic limitations are lifted, a longer, more thorough effort may find additional options. Conclusions/Implications: The ideal electromagnetic pain treatment device for use on exploration-class spaceflight missions does not yet exist, but it may be available soon. It is not feasible for NASA to develop medical devices due to the schedule constraints for pending exploration-class missions, but adapting a promising device that is already FDA-approved might be an option. Monitoring research that is ongoing at other federal agencies is recommended, and further review of the candidate PSWT device identified in this current effort may be warranted.

Carol Mullenax↗

NASA NTRSDate not supplied

Electromagnetic Pain Relief/Blocking: Feasibility Assessment

CONTEXT/BACKGROUND Astronauts use pharmaceuticals during spaceflight to manage acute and chronic pain, but use of analgesics will have drawbacks for exploration-class missions because the shelf life of these medications is limited, resupply will be curtailed, astronauts may develop tolerance and/or addiction to these medications, and side effects can include impairment of cognitive abilities. Electromagnetic devices have been developed that treat pain terrestrially by affecting neuromodulation–dubbed “electroceuticals”, these devices have varied mechanisms of action that either stimulate or suppress neural activity in the central nervous system or peripheral nerves. OBJECTIVE/PURPOSE The available literature was reviewed and FDA-approved pain treatments (both pharmacological and non-pharmacological), as well as those currently under development, were assessed for their suitability for use in exploration class spaceflight missions. DATA SOURCES Due to the COVID-19 pandemic and the resulting closure of libraries, data sources were restricted to those available digitally. Online database searches included PubMed, U.S. Patent and Trademark Office, federal grant award databases (National Aeronautics and Space Administration (NASA), Department of Defense (DoD), National Institutes of Health (NIH)), and general internet searches. More than 1,600 records were reviewed in this effort. STUDY SELECTION/ELIGIBILITY CRITERIA Targeted searches included different aspects of pain management. Priority was given to review studies, to cover as much of the available literature as possible in this limited effort. STUDY APPRAISAL AND SYNTHESIS METHODS/DATA EXTRACTION AND DATA SYNTHESIS The titles of the studies and the awards that were obtained by searching online databases were reviewed and further information was sought for the relevant titles. Abstracts or award summaries were generally available online; for journal abstracts, full text articles were either available online or were requested via interlibrary loan. RESULTS An overwhelming majority of the literature focuses on the treatment of chronic rather than acute pain because it is assumed that acute pain only rarely fails to resolve and instead transitions into chronic pain when the central nervous system becomes hypersensitized. The available electromagnetic devices marketed for pain treatment have varying levels of invasiveness, use different mechanisms of action, and have demonstrated varying efficacy when evaluated scientifically. A truly noninvasive, highly efficient device is desired for use during spaceflight. One portable, self-contained, FDA-approved device was identified that, from preliminarily assessment, best met these criteria; the device noninvasively applies pulsed shortwave therapy (PSWT) to modify pain signals from peripheral nerves, however, the device has limited battery life and the effects are relatively non-selective in type of neural signal modified. LIMITATIONS This current effort, although extensive, did not identify a comprehensive list of all alternatives for pain treatment. Once the pandemic limitations are lifted, a longer, more thorough effort may find additional options. CONCLUSIONS/IMPLICATIONS The ideal electromagnetic pain treatment device for use on exploration-class spaceflight missions does not yet exist, but it may be available soon. It is not feasible for NASA to develop medical devices due to the schedule constraints for pending exploration-class missions, but adapting a promising device that is already FDA-approved might be an option. Monitoring research that is ongoing at other federal agencies is recommended, and further review of the candidate PSWT device identified in this current effort may be warranted.

C A Mullenax↗